In [1]}, the authors present a deep learning neural network model called COVID-MTNet that uses multi-task deep learning to detect COVID-19 from X-ray images. COVID-MTNet uses multiple models for different tasks, such as the classification model for COVID-19 detection and the segmentation model for Region of Interest (ROI) detection. Furthermore, the Recurrent Residual Neural Network (RRCNN) model performs the COVID-19 detection task, and the NABLA-N network executes the infected region segmentation from X-ray and CT images. The dataset used to train the model comprises a total of 5,216 images samples. COVID-MTNet has shown 87.26% of testing accuracy that indicated good efficiency and reliability. However, data samples may be increased to validate the model's robustness and accuracy.
